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Helps us to derive novel climate data by combining two of Europe's new satellite sensors. If you have interests in physics, climate and machine learning, this is the Doctoral student position
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at the Division of Data Science and AI at the Department of Computer Science and Engineering . Join our innovative team and contribute to exciting research in theory of machine learning, in a collaborative and
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combining two of Europe's new satellite sensors. If you have interests in physics, climate and machine learning, this is the Doctoral student position for you! About us Our team is part of the Division
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. The main research problems include mathematical theory, algorithms, and machine learning (deep learning) for inverse problems in artificial intelligence, as well as application to medical problems. About the
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Join and help us to derive global forest biomass data from the European Space Agency’s Biomass satellite mission. If you have interests in remote sensing, machine learning and forests, this is the
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at the division of Computer and Network Systems , where we design secure, dependable and high-performance computer and communication systems that meet the demands of an increasingly digital and interconnected world
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computational costs by orders of magnitude and enabling breakthroughs in drug design and materials science. The position bridges machine learning and molecular science, with opportunities for collaboration
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with an education earned outside of Sweden, a 4-year Bachelor’s degree is accepted. The following experience will strengthen your application: Experience in system identification and machine learning is
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identification and machine learning is a merit. What you will do Perform research, developing your own scientific concepts and communicating the results verbally and in writing Take courses at an advanced level
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-energy devices. Using state-of-the-art electronic-structure calculations and machine learning methods, you will model these effects and contribute to the design of improved semiconductors for solar cells